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EEG microstate periodicity explained by rotating phase patterns of resting-state alpha oscillations
(2020)
Spatio-temporal patterns in electroencephalography (EEG) can be described by microstate analysis, a discrete approximation of the continuous electric field patterns produced by the cerebral cortex. Resting-state EEG microstates are largely determined by alpha frequencies (8-12 Hz) and we recently demonstrated that microstates occur periodically with twice the alpha frequency.
To understand the origin of microstate periodicity, we analyzed the analytic amplitude and the analytic phase of resting-state alpha oscillations independently. In continuous EEG data we found rotating phase patterns organized around a small number of phase singularities which varied in number and location. The spatial rotation of phase patterns occurred with the underlying alpha frequency. Phase rotors coincided with periodic microstate motifs involving the four canonical microstate maps. The analytic amplitude showed no oscillatory behaviour and was almost static across time intervals of 1-2 alpha cycles, resulting in the global pattern of a standing wave.
In n=23 healthy adults, time-lagged mutual information analysis of microstate sequences derived from amplitude and phase signals of awake eyes-closed EEG records showed that only the phase component contributed to the periodicity of microstate sequences. Phase sequences showed mutual information peaks at multiples of 50 ms and the group average had a main peak at 100 ms (10 Hz), whereas amplitude sequences had a slow and monotonous information decay. This result was confirmed by an independent approach combining temporal principal component analysis (tPCA) and autocorrelation analysis.
We reproduced our observations in a generic model of EEG oscillations composed of coupled non-linear oscillators (Stuart-Landau model). Phase-amplitude dynamics similar to experimental EEG occurred when the oscillators underwent a supercritical Hopf bifurcation, a common feature of many computational models of the alpha rhythm.
These findings explain our previous description of periodic microstate recurrence and its relation to the time scale of alpha oscillations. Moreover, our results corroborate the predictions of computational models and connect experimentally observed EEG patterns to properties of critical oscillator networks.
Objective: To evaluate the incidence and risk factors of generalized convulsive seizure (GCS)-related fractures and injuries during video-EEG monitoring.
Methods: We analyzed all GCSs in patients undergoing video-EEG-monitoring between 2007 and 2019 at epilepsy centers in Frankfurt and Marburg in relation to injuries, falls and accidents associated with GCSs. Data were gathered using video material, EEG material, and a standardized reporting form.
Results: A total of 626 GCSs from 411 patients (mean age: 33.6 years; range 3–74 years; 45.0% female) were analyzed. Severe adverse events (SAEs) such as fractures, joint luxation, corneal erosion, and teeth loosening were observed in 13 patients resulting in a risk of 2.1% per GCS (95% CI 1.2–3.4%) and 3.2% per patient (95% CI 1.8–5.2%). Except for a nasal fracture due to a fall onto the face, no SAEs were caused by falls, and all occurred in patients lying in bed without evidence of external trauma. In seven patients, vertebral body compression fractures were confirmed by imaging. This resulted in a risk of 1.1% per GCS (95% CI 0.5–2.2%) and 1.7% per patient (95% CI 0.8–3.3%). These fractures occurred within the tonic phase of a GCS and were accompanied by a characteristic cracking noise. All affected patients reported back pain spontaneously, and an increase in pain on percussion of the affected spine section.
Conclusions: GCSs are associated with a substantial risk of fractures and shoulder dislocations that are not associated with falls. GCSs accompanied by audible cracking, and resulting in back pain, should prompt clinical and imaging evaluations.